This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. It is well known that knowledge of metabolite levels in healthy and diseased tissues can provide essential information on disease progression and efficacy of therapy. For instance, metastatic tumors have increased levels of metabolites like choline and lactate compared to benign tumors. 3 dimensional Chemical Shift Imaging (3D-CSI) is an MR-based non-invasive approach used in the clinic to quantify and monitor these metabolites. A major hurdle in routine clinical use of 3D-CSI is the long acquisition time and hence the time spent by the patient in the scanner. Therefore a strong need to address this problem exists to enable clinicians to make routine use of this powerful technology. The proposed project aims to overcome this limitation by the use of compressive sensing, which has been a revolutionary invention in the past few years. This technique has been successfully implemented for MRI and promises to be a new path for reducing acquisition times for MRI scans. We plan to conduct a retrospective analysis of brain and breast CSI data sets in order to compare metabolite maps obtained with conventional k-space reconstruction method to compressive sensing based reconstruction using undersampled data. We hypothesize that by exploiting the sparsity of k-space as well as the spectral data, we may be able to reduce CSI scan times for patients by a factor of 2 without significant reduction in the quality of data.